deepblink.networks package¶
Submodules¶
Module contents¶
Networks folder.
Contains functions returning the base architectures of used models.
-
deepblink.networks.
unet
(dropout: float = 0.2, cell_size: int = 4, filters: int = 5, ndown: int = 2, l2: float = 1e-06, block: str = 'convolutional') → keras.engine.training.Model[source]¶ Unet model with second, cell size dependent encoder.
Note that “convolution” is the currently best block.
Parameters: - dropout – Percentage of dropout before each MaxPooling step.
- cell_size – Size of one cell in the prediction matrix.
- filters – Log_2 number of filters in the first inception block.
- ndown – Downsampling steps in the first encoder / decoder.
- l2 – L2 value for kernel and bias regularization.
- block – Type of block in each layer. [options: convolutional, inception, residual]